Overview

Dataset statistics

Number of variables21
Number of observations2000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory328.2 KiB
Average record size in memory168.1 B

Variable types

Numeric14
Categorical7

Dataset

DescriptionInspecting Cell Phones Prices
URL
Copyright(c) Mr. Eslam Fouad 2023

Alerts

fc is highly overall correlated with pcHigh correlation
pc is highly overall correlated with fcHigh correlation
ram is highly overall correlated with price_rangeHigh correlation
four_g is highly overall correlated with three_gHigh correlation
three_g is highly overall correlated with four_gHigh correlation
price_range is highly overall correlated with ramHigh correlation
price_range is uniformly distributedUniform
fc has 474 (23.7%) zerosZeros
pc has 101 (5.1%) zerosZeros
sc_w has 180 (9.0%) zerosZeros

Reproduction

Analysis started2023-06-13 12:28:01.259902
Analysis finished2023-06-13 12:28:59.794872
Duration58.53 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

battery_power
Real number (ℝ)

Distinct1094
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1238.5185
Minimum501
Maximum1998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-06-13T12:28:59.977942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum501
5-th percentile570.95
Q1851.75
median1226
Q31615.25
95-th percentile1930.15
Maximum1998
Range1497
Interquartile range (IQR)763.5

Descriptive statistics

Standard deviation439.41821
Coefficient of variation (CV)0.35479341
Kurtosis-1.2241439
Mean1238.5185
Median Absolute Deviation (MAD)382
Skewness0.031898472
Sum2477037
Variance193088.36
MonotonicityNot monotonic
2023-06-13T12:29:00.322596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1872 6
 
0.3%
618 6
 
0.3%
1589 6
 
0.3%
1715 5
 
0.2%
1807 5
 
0.2%
1310 5
 
0.2%
1083 5
 
0.2%
1512 5
 
0.2%
1379 5
 
0.2%
1949 5
 
0.2%
Other values (1084) 1947
97.4%
ValueCountFrequency (%)
501 2
 
0.1%
502 2
 
0.1%
503 3
0.1%
504 5
0.2%
506 1
 
0.1%
507 2
 
0.1%
508 3
0.1%
509 1
 
0.1%
510 3
0.1%
511 4
0.2%
ValueCountFrequency (%)
1998 1
 
0.1%
1997 1
 
0.1%
1996 2
0.1%
1995 2
0.1%
1994 3
0.1%
1993 1
 
0.1%
1992 2
0.1%
1991 4
0.2%
1989 2
0.1%
1988 1
 
0.1%

blue
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
0
1010 
1
990 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 1010
50.5%
1 990
49.5%

Length

2023-06-13T12:29:00.662292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T12:29:00.973601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1010
50.5%
1 990
49.5%

Most occurring characters

ValueCountFrequency (%)
0 1010
50.5%
1 990
49.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1010
50.5%
1 990
49.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1010
50.5%
1 990
49.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1010
50.5%
1 990
49.5%

clock_speed
Real number (ℝ)

Distinct26
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.52225
Minimum0.5
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-06-13T12:29:01.234393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5
Q10.7
median1.5
Q32.2
95-th percentile2.8
Maximum3
Range2.5
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation0.81600421
Coefficient of variation (CV)0.53605138
Kurtosis-1.3234172
Mean1.52225
Median Absolute Deviation (MAD)0.8
Skewness0.17808412
Sum3044.5
Variance0.66586287
MonotonicityNot monotonic
2023-06-13T12:29:01.555646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.5 413
20.6%
2.8 85
 
4.2%
2.3 78
 
3.9%
2.1 76
 
3.8%
1.6 76
 
3.8%
2.5 74
 
3.7%
0.6 74
 
3.7%
1.4 70
 
3.5%
1.3 68
 
3.4%
1.5 67
 
3.4%
Other values (16) 919
46.0%
ValueCountFrequency (%)
0.5 413
20.6%
0.6 74
 
3.7%
0.7 64
 
3.2%
0.8 58
 
2.9%
0.9 58
 
2.9%
1 61
 
3.0%
1.1 51
 
2.5%
1.2 56
 
2.8%
1.3 68
 
3.4%
1.4 70
 
3.5%
ValueCountFrequency (%)
3 28
 
1.4%
2.9 62
3.1%
2.8 85
4.2%
2.7 55
2.8%
2.6 55
2.8%
2.5 74
3.7%
2.4 58
2.9%
2.3 78
3.9%
2.2 59
2.9%
2.1 76
3.8%

dual_sim
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1019 
0
981 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 1019
50.9%
0 981
49.0%

Length

2023-06-13T12:29:01.858065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T12:29:02.188738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 1019
50.9%
0 981
49.0%

Most occurring characters

ValueCountFrequency (%)
1 1019
50.9%
0 981
49.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1019
50.9%
0 981
49.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1019
50.9%
0 981
49.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1019
50.9%
0 981
49.0%

fc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3095
Minimum0
Maximum19
Zeros474
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-06-13T12:29:02.450672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile13
Maximum19
Range19
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.3414437
Coefficient of variation (CV)1.0074124
Kurtosis0.27707632
Mean4.3095
Median Absolute Deviation (MAD)3
Skewness1.0198114
Sum8619
Variance18.848134
MonotonicityNot monotonic
2023-06-13T12:29:02.753335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 474
23.7%
1 245
12.2%
2 189
 
9.4%
3 170
 
8.5%
5 139
 
7.0%
4 133
 
6.7%
6 112
 
5.6%
7 100
 
5.0%
9 78
 
3.9%
8 77
 
3.9%
Other values (10) 283
14.1%
ValueCountFrequency (%)
0 474
23.7%
1 245
12.2%
2 189
 
9.4%
3 170
 
8.5%
4 133
 
6.7%
5 139
 
7.0%
6 112
 
5.6%
7 100
 
5.0%
8 77
 
3.9%
9 78
 
3.9%
ValueCountFrequency (%)
19 1
 
0.1%
18 11
 
0.5%
17 6
 
0.3%
16 24
 
1.2%
15 23
 
1.1%
14 20
 
1.0%
13 40
2.0%
12 45
2.2%
11 51
2.5%
10 62
3.1%

four_g
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1043 
0
957 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 1043
52.1%
0 957
47.9%

Length

2023-06-13T12:29:03.074730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T12:29:03.376510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 1043
52.1%
0 957
47.9%

Most occurring characters

ValueCountFrequency (%)
1 1043
52.1%
0 957
47.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1043
52.1%
0 957
47.9%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1043
52.1%
0 957
47.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1043
52.1%
0 957
47.9%

int_memory
Real number (ℝ)

Distinct63
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.0465
Minimum2
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-06-13T12:29:03.656007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q116
median32
Q348
95-th percentile61
Maximum64
Range62
Interquartile range (IQR)32

Descriptive statistics

Standard deviation18.145715
Coefficient of variation (CV)0.56623079
Kurtosis-1.216074
Mean32.0465
Median Absolute Deviation (MAD)16
Skewness0.057889328
Sum64093
Variance329.26697
MonotonicityNot monotonic
2023-06-13T12:29:03.995921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 47
 
2.4%
16 45
 
2.2%
14 45
 
2.2%
57 42
 
2.1%
2 42
 
2.1%
42 40
 
2.0%
7 40
 
2.0%
44 39
 
1.9%
30 39
 
1.9%
6 37
 
1.8%
Other values (53) 1584
79.2%
ValueCountFrequency (%)
2 42
2.1%
3 25
1.2%
4 20
1.0%
5 36
1.8%
6 37
1.8%
7 40
2.0%
8 37
1.8%
9 35
1.8%
10 36
1.8%
11 34
1.7%
ValueCountFrequency (%)
64 31
1.6%
63 30
1.5%
62 21
1.1%
61 27
1.4%
60 27
1.4%
59 18
0.9%
58 36
1.8%
57 42
2.1%
56 27
1.4%
55 29
1.5%

m_dep
Real number (ℝ)

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50175
Minimum0.1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-06-13T12:29:04.304228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.2
median0.5
Q30.8
95-th percentile1
Maximum1
Range0.9
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.28841555
Coefficient of variation (CV)0.57481923
Kurtosis-1.2743489
Mean0.50175
Median Absolute Deviation (MAD)0.3
Skewness0.08908201
Sum1003.5
Variance0.083183529
MonotonicityNot monotonic
2023-06-13T12:29:04.574159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.1 320
16.0%
0.2 213
10.7%
0.8 208
10.4%
0.5 205
10.2%
0.7 200
10.0%
0.3 199
10.0%
0.9 195
9.8%
0.6 186
9.3%
0.4 168
8.4%
1 106
 
5.3%
ValueCountFrequency (%)
0.1 320
16.0%
0.2 213
10.7%
0.3 199
10.0%
0.4 168
8.4%
0.5 205
10.2%
0.6 186
9.3%
0.7 200
10.0%
0.8 208
10.4%
0.9 195
9.8%
1 106
 
5.3%
ValueCountFrequency (%)
1 106
 
5.3%
0.9 195
9.8%
0.8 208
10.4%
0.7 200
10.0%
0.6 186
9.3%
0.5 205
10.2%
0.4 168
8.4%
0.3 199
10.0%
0.2 213
10.7%
0.1 320
16.0%

mobile_wt
Real number (ℝ)

Distinct121
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.249
Minimum80
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-06-13T12:29:04.871404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile86
Q1109
median141
Q3170
95-th percentile196
Maximum200
Range120
Interquartile range (IQR)61

Descriptive statistics

Standard deviation35.399655
Coefficient of variation (CV)0.25240576
Kurtosis-1.2103765
Mean140.249
Median Absolute Deviation (MAD)31
Skewness0.0065581574
Sum280498
Variance1253.1356
MonotonicityNot monotonic
2023-06-13T12:29:05.242646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
182 28
 
1.4%
101 27
 
1.4%
185 27
 
1.4%
146 26
 
1.3%
199 26
 
1.3%
88 25
 
1.2%
198 25
 
1.2%
105 25
 
1.2%
89 24
 
1.2%
131 23
 
1.1%
Other values (111) 1744
87.2%
ValueCountFrequency (%)
80 21
1.1%
81 13
0.7%
82 15
0.8%
83 19
0.9%
84 17
0.9%
85 13
0.7%
86 19
0.9%
87 15
0.8%
88 25
1.2%
89 24
1.2%
ValueCountFrequency (%)
200 19
0.9%
199 26
1.3%
198 25
1.2%
197 19
0.9%
196 20
1.0%
195 11
0.5%
194 16
0.8%
193 15
0.8%
192 15
0.8%
191 15
0.8%

n_cores
Real number (ℝ)

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5205
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-06-13T12:29:05.546266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q37
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2878367
Coefficient of variation (CV)0.50610258
Kurtosis-1.2297498
Mean4.5205
Median Absolute Deviation (MAD)2
Skewness0.0036275083
Sum9041
Variance5.2341968
MonotonicityNot monotonic
2023-06-13T12:29:05.803417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 274
13.7%
7 259
13.0%
8 256
12.8%
2 247
12.3%
3 246
12.3%
5 246
12.3%
1 242
12.1%
6 230
11.5%
ValueCountFrequency (%)
1 242
12.1%
2 247
12.3%
3 246
12.3%
4 274
13.7%
5 246
12.3%
6 230
11.5%
7 259
13.0%
8 256
12.8%
ValueCountFrequency (%)
8 256
12.8%
7 259
13.0%
6 230
11.5%
5 246
12.3%
4 274
13.7%
3 246
12.3%
2 247
12.3%
1 242
12.1%

pc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9165
Minimum0
Maximum20
Zeros101
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-06-13T12:29:06.113257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median10
Q315
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.0643149
Coefficient of variation (CV)0.61153784
Kurtosis-1.1714988
Mean9.9165
Median Absolute Deviation (MAD)5
Skewness0.01730615
Sum19833
Variance36.775916
MonotonicityNot monotonic
2023-06-13T12:29:07.144705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
10 122
 
6.1%
7 119
 
5.9%
9 112
 
5.6%
20 110
 
5.5%
1 104
 
5.2%
14 104
 
5.2%
0 101
 
5.1%
2 99
 
5.0%
17 99
 
5.0%
6 95
 
4.8%
Other values (11) 935
46.8%
ValueCountFrequency (%)
0 101
5.1%
1 104
5.2%
2 99
5.0%
3 93
4.7%
4 95
4.8%
5 59
2.9%
6 95
4.8%
7 119
5.9%
8 89
4.5%
9 112
5.6%
ValueCountFrequency (%)
20 110
5.5%
19 83
4.2%
18 82
4.1%
17 99
5.0%
16 88
4.4%
15 92
4.6%
14 104
5.2%
13 85
4.2%
12 90
4.5%
11 79
4.0%

px_height
Real number (ℝ)

Distinct1137
Distinct (%)56.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean645.108
Minimum0
Maximum1960
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-06-13T12:29:07.491017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile70.95
Q1282.75
median564
Q3947.25
95-th percentile1485.05
Maximum1960
Range1960
Interquartile range (IQR)664.5

Descriptive statistics

Standard deviation443.78081
Coefficient of variation (CV)0.68791708
Kurtosis-0.31586549
Mean645.108
Median Absolute Deviation (MAD)318
Skewness0.66627126
Sum1290216
Variance196941.41
MonotonicityNot monotonic
2023-06-13T12:29:07.862212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
347 7
 
0.4%
179 6
 
0.3%
371 6
 
0.3%
275 6
 
0.3%
674 5
 
0.2%
286 5
 
0.2%
42 5
 
0.2%
211 5
 
0.2%
649 5
 
0.2%
398 5
 
0.2%
Other values (1127) 1945
97.2%
ValueCountFrequency (%)
0 2
0.1%
1 1
 
0.1%
2 1
 
0.1%
3 2
0.1%
4 3
0.1%
5 1
 
0.1%
6 1
 
0.1%
7 1
 
0.1%
8 2
0.1%
9 1
 
0.1%
ValueCountFrequency (%)
1960 1
0.1%
1949 1
0.1%
1920 1
0.1%
1914 1
0.1%
1901 1
0.1%
1899 1
0.1%
1895 1
0.1%
1878 1
0.1%
1874 1
0.1%
1869 1
0.1%

px_width
Real number (ℝ)

Distinct1109
Distinct (%)55.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1251.5155
Minimum500
Maximum1998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-06-13T12:29:08.251265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile579.85
Q1874.75
median1247
Q31633
95-th percentile1929.05
Maximum1998
Range1498
Interquartile range (IQR)758.25

Descriptive statistics

Standard deviation432.19945
Coefficient of variation (CV)0.34534087
Kurtosis-1.1860052
Mean1251.5155
Median Absolute Deviation (MAD)376
Skewness0.014787474
Sum2503031
Variance186796.36
MonotonicityNot monotonic
2023-06-13T12:29:08.602860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
874 7
 
0.4%
1247 7
 
0.4%
1383 6
 
0.3%
1463 6
 
0.3%
1469 6
 
0.3%
1393 5
 
0.2%
1781 5
 
0.2%
1767 5
 
0.2%
1923 5
 
0.2%
1429 5
 
0.2%
Other values (1099) 1943
97.2%
ValueCountFrequency (%)
500 2
0.1%
501 2
0.1%
503 1
 
0.1%
506 1
 
0.1%
507 4
0.2%
508 1
 
0.1%
509 2
0.1%
510 3
0.1%
511 2
0.1%
512 2
0.1%
ValueCountFrequency (%)
1998 1
 
0.1%
1997 1
 
0.1%
1996 1
 
0.1%
1995 3
0.1%
1994 2
 
0.1%
1992 1
 
0.1%
1991 1
 
0.1%
1990 1
 
0.1%
1989 3
0.1%
1988 5
0.2%

ram
Real number (ℝ)

Distinct1562
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2124.213
Minimum256
Maximum3998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-06-13T12:29:08.962976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum256
5-th percentile445
Q11207.5
median2146.5
Q33064.5
95-th percentile3826.35
Maximum3998
Range3742
Interquartile range (IQR)1857

Descriptive statistics

Standard deviation1084.732
Coefficient of variation (CV)0.51065126
Kurtosis-1.1919131
Mean2124.213
Median Absolute Deviation (MAD)932.5
Skewness0.0066280354
Sum4248426
Variance1176643.6
MonotonicityNot monotonic
2023-06-13T12:29:09.313576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1464 4
 
0.2%
3142 4
 
0.2%
2610 4
 
0.2%
2227 4
 
0.2%
1229 4
 
0.2%
3654 3
 
0.1%
1277 3
 
0.1%
1050 3
 
0.1%
2775 3
 
0.1%
2674 3
 
0.1%
Other values (1552) 1965
98.2%
ValueCountFrequency (%)
256 1
0.1%
258 2
0.1%
259 1
0.1%
262 1
0.1%
263 1
0.1%
265 1
0.1%
267 1
0.1%
273 1
0.1%
277 1
0.1%
278 2
0.1%
ValueCountFrequency (%)
3998 1
0.1%
3996 1
0.1%
3993 1
0.1%
3991 2
0.1%
3990 1
0.1%
3984 1
0.1%
3978 1
0.1%
3971 1
0.1%
3970 2
0.1%
3969 1
0.1%

sc_h
Real number (ℝ)

Distinct15
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.3065
Minimum5
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-06-13T12:29:09.633453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q19
median12
Q316
95-th percentile19
Maximum19
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.213245
Coefficient of variation (CV)0.34235932
Kurtosis-1.1907912
Mean12.3065
Median Absolute Deviation (MAD)4
Skewness-0.098884241
Sum24613
Variance17.751433
MonotonicityNot monotonic
2023-06-13T12:29:09.892401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
17 193
 
9.7%
12 157
 
7.8%
7 151
 
7.5%
16 143
 
7.1%
14 143
 
7.1%
15 135
 
6.8%
13 131
 
6.6%
11 126
 
6.3%
10 125
 
6.2%
9 124
 
6.2%
Other values (5) 572
28.6%
ValueCountFrequency (%)
5 97
4.9%
6 114
5.7%
7 151
7.5%
8 117
5.9%
9 124
6.2%
10 125
6.2%
11 126
6.3%
12 157
7.8%
13 131
6.6%
14 143
7.1%
ValueCountFrequency (%)
19 124
6.2%
18 120
6.0%
17 193
9.7%
16 143
7.1%
15 135
6.8%
14 143
7.1%
13 131
6.6%
12 157
7.8%
11 126
6.3%
10 125
6.2%

sc_w
Real number (ℝ)

Distinct19
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.767
Minimum0
Maximum18
Zeros180
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-06-13T12:29:10.183793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile14
Maximum18
Range18
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.3563976
Coefficient of variation (CV)0.75540101
Kurtosis-0.38952279
Mean5.767
Median Absolute Deviation (MAD)3
Skewness0.63378707
Sum11534
Variance18.9782
MonotonicityNot monotonic
2023-06-13T12:29:10.456811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 210
10.5%
3 199
10.0%
4 182
9.1%
0 180
9.0%
5 161
 
8.1%
2 156
 
7.8%
7 132
 
6.6%
6 130
 
6.5%
8 125
 
6.2%
10 107
 
5.3%
Other values (9) 418
20.9%
ValueCountFrequency (%)
0 180
9.0%
1 210
10.5%
2 156
7.8%
3 199
10.0%
4 182
9.1%
5 161
8.1%
6 130
6.5%
7 132
6.6%
8 125
6.2%
9 97
4.9%
ValueCountFrequency (%)
18 8
 
0.4%
17 19
 
0.9%
16 29
 
1.5%
15 31
 
1.6%
14 33
 
1.7%
13 49
2.5%
12 68
3.4%
11 84
4.2%
10 107
5.3%
9 97
4.9%

talk_time
Real number (ℝ)

Distinct19
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.011
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-06-13T12:29:10.745632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q16
median11
Q316
95-th percentile20
Maximum20
Range18
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.4639552
Coefficient of variation (CV)0.49622697
Kurtosis-1.218591
Mean11.011
Median Absolute Deviation (MAD)5
Skewness0.0095117622
Sum22022
Variance29.854806
MonotonicityNot monotonic
2023-06-13T12:29:11.032138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
7 124
 
6.2%
4 123
 
6.2%
16 116
 
5.8%
15 115
 
5.8%
19 113
 
5.7%
6 111
 
5.5%
10 105
 
5.2%
8 104
 
5.2%
11 103
 
5.1%
20 102
 
5.1%
Other values (9) 884
44.2%
ValueCountFrequency (%)
2 99
5.0%
3 94
4.7%
4 123
6.2%
5 93
4.7%
6 111
5.5%
7 124
6.2%
8 104
5.2%
9 100
5.0%
10 105
5.2%
11 103
5.1%
ValueCountFrequency (%)
20 102
5.1%
19 113
5.7%
18 100
5.0%
17 98
4.9%
16 116
5.8%
15 115
5.8%
14 101
5.1%
13 100
5.0%
12 99
5.0%
11 103
5.1%

three_g
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1523 
0
477 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1523
76.1%
0 477
 
23.8%

Length

2023-06-13T12:29:11.351935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T12:29:11.654845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 1523
76.1%
0 477
 
23.8%

Most occurring characters

ValueCountFrequency (%)
1 1523
76.1%
0 477
 
23.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1523
76.1%
0 477
 
23.8%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1523
76.1%
0 477
 
23.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1523
76.1%
0 477
 
23.8%

touch_screen
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1006 
0
994 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 1006
50.3%
0 994
49.7%

Length

2023-06-13T12:29:11.912818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T12:29:12.222309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 1006
50.3%
0 994
49.7%

Most occurring characters

ValueCountFrequency (%)
1 1006
50.3%
0 994
49.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1006
50.3%
0 994
49.7%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1006
50.3%
0 994
49.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1006
50.3%
0 994
49.7%

wifi
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1014 
0
986 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 1014
50.7%
0 986
49.3%

Length

2023-06-13T12:29:12.487595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T12:29:12.790689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 1014
50.7%
0 986
49.3%

Most occurring characters

ValueCountFrequency (%)
1 1014
50.7%
0 986
49.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1014
50.7%
0 986
49.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1014
50.7%
0 986
49.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1014
50.7%
0 986
49.3%

price_range
Categorical

HIGH CORRELATION  UNIFORM 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
500 
2
500 
3
500 
0
500 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 500
25.0%
2 500
25.0%
3 500
25.0%
0 500
25.0%

Length

2023-06-13T12:29:13.062932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T12:29:13.383434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 500
25.0%
2 500
25.0%
3 500
25.0%
0 500
25.0%

Most occurring characters

ValueCountFrequency (%)
1 500
25.0%
2 500
25.0%
3 500
25.0%
0 500
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 500
25.0%
2 500
25.0%
3 500
25.0%
0 500
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 500
25.0%
2 500
25.0%
3 500
25.0%
0 500
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 500
25.0%
2 500
25.0%
3 500
25.0%
0 500
25.0%

Interactions

2023-06-13T12:28:54.659235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:11.107671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:14.848401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:18.332230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:22.017700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:24.771136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:27.419091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:30.691578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:33.470776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:36.567789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:40.715220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:44.992233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:47.801520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:50.631556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:54.952740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:11.413675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:15.053011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:18.620103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:22.232006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:24.968277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:27.628768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:30.896796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:33.673256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:36.861548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:41.008642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:45.203856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:47.999043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:50.922489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:55.240453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:11.705164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:15.231765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:18.899368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:22.421574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:25.150815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:27.819227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:31.090867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:33.865349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:37.141715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:41.290528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:45.397385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:48.184843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:51.200311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:55.530206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:11.999205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:15.872892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:19.186661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:22.621679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:25.342773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:28.551557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:31.292613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:34.066737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:37.385014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:41.582260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:45.598193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:48.382465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:51.489060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:55.812345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:12.290834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:16.066280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:19.463418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:22.809813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:25.521112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:28.739350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:31.493810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:34.262391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:37.676359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:41.870176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:45.790681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:48.571107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:51.772137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:56.092733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:12.564203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:16.240785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:19.732029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:22.991622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:25.695624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:28.917192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:31.679660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:34.448197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:37.973871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:42.156164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:45.976811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:48.747991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:52.054719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:56.374525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:12.851361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:16.427344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:20.010462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:23.181751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:25.883634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:29.102612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:31.879009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:34.644412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:38.275287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:42.464878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:46.168543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:48.948840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:52.334625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:56.662116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:13.145092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:16.621599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:20.296938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:23.379947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:26.076705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:29.295264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:32.089281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:34.847079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:38.582069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:42.766336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:46.377078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:49.154306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:52.620960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:56.960077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:13.440083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:16.817117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:20.588841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:23.594033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:26.274170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:29.499500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:32.291851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:35.052575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:38.897231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:43.071935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:46.590842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:49.366094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:52.920254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:57.263820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:13.744573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:17.020922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:20.880716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:23.798772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:26.469387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:29.702022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:32.491072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:35.265808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:39.202960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:43.363075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:46.795782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:49.574038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:53.217386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:57.548373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:14.010672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:17.220094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:21.169209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:23.995698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:26.659126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:29.898851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:32.686320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:35.470230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:39.512513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:43.565633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:47.000326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:49.764433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:53.511955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:57.838374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:14.228687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:17.473001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:21.420633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:24.193380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:26.847451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:30.097910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:32.881307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:35.673418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:39.810431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:43.773445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:47.197844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:49.959824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:53.805989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:58.127086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:14.431382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:17.758604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:21.611515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:24.380702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:27.031519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:30.289949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:33.068800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:35.950500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:40.117682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:43.974237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:47.395204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:50.143699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:54.089824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:58.411922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:14.641756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:18.044646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:21.809576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:24.575123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:27.222027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:30.492659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:33.265848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:36.270038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:40.414958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:44.184639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:47.598155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:50.336908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T12:28:54.370150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-13T12:29:13.661628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
battery_powerclock_speedfcint_memorym_depmobile_wtn_corespcpx_heightpx_widthramsc_hsc_wtalk_timebluedual_simfour_gthree_gtouch_screenwifiprice_range
battery_power1.0000.0090.035-0.0040.0330.002-0.0300.0310.009-0.009-0.001-0.029-0.0270.0530.0340.0530.0000.0000.0000.0000.128
clock_speed0.0091.000-0.0050.005-0.0150.011-0.008-0.006-0.013-0.0090.004-0.030-0.015-0.0130.0500.0220.0460.0370.0340.0000.000
fc0.035-0.0051.000-0.0270.0130.027-0.0160.659-0.021-0.0090.020-0.010-0.001-0.0010.0000.0000.0380.0000.0460.0490.000
int_memory-0.0040.005-0.0271.0000.007-0.034-0.028-0.033-0.002-0.0090.0330.0400.016-0.0020.0570.0000.0000.0280.0220.0100.040
m_dep0.033-0.0150.0130.0071.0000.022-0.0050.0280.0260.023-0.010-0.024-0.0190.0170.0280.0610.0000.0000.0680.0160.017
mobile_wt0.0020.0110.027-0.0340.0221.000-0.0190.0190.0110.001-0.003-0.034-0.0190.0060.0000.0360.0480.0000.0000.0000.027
n_cores-0.030-0.008-0.016-0.028-0.005-0.0191.000-0.002-0.0050.0240.0050.0010.0290.0130.0000.0000.0410.0220.0000.0000.000
pc0.031-0.0060.659-0.0330.0280.019-0.0021.000-0.0150.0030.0290.005-0.0350.0140.0000.0000.0000.0000.0330.0000.029
px_height0.009-0.013-0.021-0.0020.0260.011-0.005-0.0151.0000.468-0.0310.0540.029-0.0100.0000.0000.0210.0250.0000.0630.084
px_width-0.009-0.009-0.009-0.0090.0230.0010.0240.0030.4681.0000.0030.0230.0250.0070.0000.0000.0000.0000.0000.0370.105
ram-0.0010.0040.0200.033-0.010-0.0030.0050.029-0.0310.0031.0000.0160.0260.0120.0000.0230.0070.0430.0000.0000.723
sc_h-0.029-0.030-0.0100.040-0.024-0.0340.0010.0050.0540.0230.0161.0000.470-0.0180.0000.0000.0810.0220.0090.0700.034
sc_w-0.027-0.015-0.0010.016-0.019-0.0190.029-0.0350.0290.0250.0260.4701.000-0.0220.0340.0110.0000.0470.0000.0000.060
talk_time0.053-0.013-0.001-0.0020.0170.0060.0130.014-0.0100.0070.012-0.018-0.0221.0000.0000.0140.0420.0360.0500.0000.000
blue0.0340.0500.0000.0570.0280.0000.0000.0000.0000.0000.0000.0000.0340.0001.0000.0260.0000.0190.0000.0000.000
dual_sim0.0530.0220.0000.0000.0610.0360.0000.0000.0000.0000.0230.0000.0110.0140.0261.0000.0000.0000.0000.0000.000
four_g0.0000.0460.0380.0000.0000.0480.0410.0000.0210.0000.0070.0810.0000.0420.0000.0001.0000.5830.0000.0000.009
three_g0.0000.0370.0000.0280.0000.0000.0220.0000.0250.0000.0430.0220.0470.0360.0190.0000.5831.0000.0000.0000.000
touch_screen0.0000.0340.0460.0220.0680.0000.0000.0330.0000.0000.0000.0090.0000.0500.0000.0000.0000.0001.0000.0000.021
wifi0.0000.0000.0490.0100.0160.0000.0000.0000.0630.0370.0000.0700.0000.0000.0000.0000.0000.0000.0001.0000.000
price_range0.1280.0000.0000.0400.0170.0270.0000.0290.0840.1050.7230.0340.0600.0000.0000.0000.0090.0000.0210.0001.000

Missing values

2023-06-13T12:28:58.864660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-13T12:28:59.482729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

battery_powerblueclock_speeddual_simfcfour_gint_memorym_depmobile_wtn_corespcpx_heightpx_widthramsc_hsc_wtalk_timethree_gtouch_screenwifiprice_range
084202.201070.61882220756254997190011
1102110.5101530.7136369051988263117371102
256310.5121410.91455612631716260311291102
361512.5000100.813169121617862769168111002
4182111.20131440.614121412081212141182151101
5185900.5130220.716417100416541067171101001
6182101.7041100.813981038110183220138181013
7195400.5100240.818740512114970016351110
8144510.5000530.71747143868361099171201000
950910.612190.193515113712245131910121000
battery_powerblueclock_speeddual_simfcfour_gint_memorym_depmobile_wtn_corespcpx_heightpx_widthramsc_hsc_wtalk_timethree_gtouch_screenwifiprice_range
1990161712.4081360.8851974314262965371000
1991188202.00111440.811381947433579198201103
199267412.9110210.219834576180911806341110
1993146710.5000180.61225088810993962151151113
199485802.2010500.1841252814163978171631103
199579410.510120.810661412221890668134191100
1996196512.6100390.218743915196520321110161112
1997191100.9111360.710883868163230579151103
1998151200.9041460.1145553366708691810191110
199951012.0151450.9168616483754391919421113